Plant health monitoring, early warning and processing system and method based on volatile matters

Abstract

The invention discloses a plant health monitoring, early warning and processing system and method based on volatile matters, and the system comprises the following modules which are connected in sequence: a volatile matter collection and identification module which is used for collecting plant volatile matters, environmental parameters and position information through multiple modes; the volatile matter integrated analysis module comprises a plant volatile matter database and a feedback unit and is used for analyzing volatile matter data and associating the volatile matter data with plant health and pest and disease damage states; the plant health and pest and disease damage early warning module comprises an early warning model and a feedback unit and is used for predicting the plant health and pest and disease damage development trend based on the analysis result; and the reaction processing module comprises a prevention and control database and a feedback unit and is used for providing targeted cultivation or prevention and control suggestions. Early monitoring of plant diseases and insect pests is achieved, and timeliness is improved; the volatile data application is promoted; monitoring and prevention and control are integrated, and invalid pesticide application is reduced; and in addition, multi-scene and multi-device collaboration can be supported.

Description

Technical Field

[0001] The present invention relates to the technical field of plant health monitoring, and more particularly to a plant health monitoring, early warning and processing system and method based on volatile substances. Background Art

[0002] Plants are affected by abiotic factors such as environmental temperature and humidity, as well as by biotic factors such as pests and diseases, and will produce various specific volatiles. Plant volatiles can directly reflect the growth and health status of plants. Currently, more than 1,700 plant volatiles have been identified, but we are still in the stage of collecting and identifying plant volatiles. The collection methods and equipment are less timely and sensitive, and there is basically no systematic data analysis and application.

[0003] In related technologies, plant health monitoring and early warning are primarily divided into two areas: pest monitoring and disease monitoring. Pest monitoring uses attractants, light sources, and other methods to trap pests and provide early warning of their presence. Disease monitoring primarily involves collecting spores of fungal pathogens to identify the pathogen type and disease occurrence, or monitoring plant disease symptoms through photographs of plants or remote spectral scanning using drones. Most disease monitoring relies on manual labor, and spore monitoring is only applicable to a few diseases. This generally fails to provide early monitoring and early warning, nor does it allow for preventive measures or early control measures after early warnings.

[0004] Currently, there is no technology to systematically monitor and warn of plant volatiles, plant diseases, pests, and plant health. However, with advances in nanosensors, the Internet of Things, and artificial intelligence, systematic monitoring of plant health and early warning, prevention, and control using changes in plant volatiles or their specific odors will rapidly emerge.

[0005] However, there are still at least the following problems:

[0006] 1) Insufficient utilization and application of plant volatiles. This is manifested in two aspects. On the one hand, although more than 1,700 plant volatiles have been identified, they remain at the basic recognition stage. There is no "odor model" to fully correspond them with the environmental stresses and pest and disease biological stresses of different plants and crops, and to play a role in production practice. On the other hand, although existing research has identified more than 1,700 volatiles, due to the limitations of previous technology, the accuracy and richness of plant volatile identification need to be further improved.

[0007] 2) Plant health monitoring and early warning are not timely. On the one hand, plant and crop monitoring often uses plant diseases and pests as monitoring indicators, and rarely monitors plant health status. On the other hand, plant disease monitoring, which uses video recording or spore collection, can only capture images or spores after the pathogens that infect the plant have accumulated to a certain level. Other monitoring methods can basically only detect the disease after it has occurred or has become severe. This makes it impossible to establish an effective early warning model, fail to effectively guide early prevention, and limit the effectiveness of prevention and control. This does not conform to the basic concept of plant protection, which focuses on prevention.

[0008] 3) Lack of post-processing response to plant health monitoring. Currently, due to a lag in monitoring plant diseases, pests, and plant health, effective early warning plant protection measures have not been developed, such as guidance on the early application of insecticides and fungicides, or guidance on fertilization or the application of immune-inducing substances based on plant health conditions. Consequently, the original intention of plant health or pest and disease monitoring has not been fully realized. Farmers still adhere to traditional practices of frequent and fixed application of pesticides during the growing season, increasing ineffective application. Summary of the Invention

[0009] In view of this, the present invention provides a plant health monitoring, early warning and processing system and method based on volatiles, which combines sensor technology, database modeling and the Internet of Things to achieve early warning and precise prevention and control of plant diseases and insect pests, and can solve the above technical problems.

[0010] In order to achieve the above object, the present invention adopts the following technical solutions:

[0011] An embodiment of the present invention provides a volatile-based plant health monitoring, early warning, and processing system, comprising the following modules connected in sequence:

[0012] Volatile collection and identification module, used to collect plant volatiles, environmental parameters and location information through multi-mode;

[0013] The volatiles integrated analysis module includes a plant volatiles database and a feedback unit for analyzing volatiles data and correlating them with plant health and pest and disease status;

[0014] Plant health and pest warning module, which includes warning models and feedback units, and is used to predict plant health and pest development trends based on analysis results;

[0015] The response processing module includes a control database and a feedback unit, which is used to provide targeted cultivation or control suggestions.

[0016] Furthermore, the volatile matter collection and identification module includes:

[0017] At least one volatile matter collection unit, using a headspace adsorption device combined with a portable gas phase analysis device or sensor, wherein the sensor material is selected from metal oxides, metal nanomaterials, graphene, carbon nanomaterials or polymers;

[0018] At least one environmental factor collection unit for collecting temperature and humidity data;

[0019] At least one location information collection unit, used to locate the collection point;

[0020] The volatile database module is used to store the volatile data of the target crops and recommend specific volatile collection units based on the target crop types and volatile types;

[0021] The working modes of the volatile matter collection and identification module include field fixed, handheld or equipment-carried collection.

[0022] Furthermore, the equipment-carrying collection device is vehicle-mounted or drone-mounted, and is used for large-scale cruise collection.

[0023] Furthermore, the plant volatile database includes:

[0024] The health status sub-library records the volatile feature vectors of plant health and sub-health status;

[0025] The pest and disease sub-library stores specific volatile fingerprints according to pathogen types and infection stages;

[0026] The dynamic update unit automatically captures newly disclosed volatile compound data and refines it to health level, infection days and environmental conditions, or issues corresponding reminders when there is no relevant information for the automatically captured newly disclosed volatile compounds; and finally generates a multi-dimensional feature vector.

[0027] Furthermore, the feedback unit of the volatiles integrated analysis module uses a principal component analysis dimensionality reduction algorithm to reduce the 100+ dimensional volatile parameters to 10-15 dimensions, generate a visual report and output it through a mobile terminal.

[0028] Furthermore, the early warning model of the plant health and pest and disease early warning module includes:

[0029] Long short-term memory network model for time series prediction of infection stages;

[0030] Support vector machine model for disease type classification;

[0031] The model input parameters include volatile matter concentration gradient, environmental factors and geographical location data.

[0032] Furthermore, the feedback unit of the plant health and pest warning module is used to generate a multi-level response signal according to the warning level, including: when the concentration of the characteristic component exceeds the healthy average by N times, an initial warning is triggered; when it exceeds 2N times, an emergency warning is triggered; and the key contributing components are analyzed through the SHAP value, and presented in the form of a mobile phone, computer or color card, and the warning location is included.

[0033] Furthermore, the control database of the reaction processing module includes:

[0034] Immunity induction program library, including immunity induction programs targeting plant health status, as well as biological or chemical control programs for pests and diseases;

[0035] A precise pesticide application plan library that matches biological pesticides and application coordinates according to the type of pests and diseases.

[0036] Furthermore, the reaction processing module is also used to cooperate with the drone spraying system to achieve targeted spraying through geo-fencing technology.

[0037] In a second aspect, an embodiment of the present invention further provides a volatile-based plant health monitoring, early warning, and treatment method, using the volatile-based plant health monitoring, early warning, and treatment system as described in any one of the first aspects, comprising the following steps:

[0038] S1. Obtain volatiles, environment and location data through the volatiles collection and identification module;

[0039] S2. Generate plant health or pest and disease status reports by matching the database with the volatiles integrated analysis module;

[0040] S3. Predict plant health and pest and disease development trends through the plant health and pest and disease early warning module;

[0041] S4. Generate prevention and control recommendations and guide their implementation through the response processing module.

[0042] The above technical solutions show that compared with existing technologies, the present invention has the following technical advantages: it realizes early monitoring of plant diseases and insect pests, improving timeliness; promotes data application by building a volatile database and early warning model; integrates monitoring and prevention, reducing ineffective pesticide application; and can support multiple scenarios (fields, facilities, orchards, forests) and multi-device collaboration. Furthermore, it has the following technical effects:

[0043] 1) From the perspective of plant volatiles, previous work has only remained at the stage of collecting plant volatiles and has not risen to the level of application. The present invention proposes a systematic and overall approach to the development and utilization of plant volatiles, such as improving the sensitivity of volatile collection, the multi-faceted integrated utilization of volatile collection units, and airborne remote monitoring. These have put forward new requirements for work in this direction and are conducive to promoting the rapid development of work in this direction.

[0044] 2) From the perspective of monitoring and early warning, this is an innovation in the field of plant disease monitoring. In the past, disease monitoring using images or spore capture could only be achieved after the disease had reached a certain level, and spore capture was only effective for airborne diseases. This invention utilizes the fact that plants can exhibit changes in volatiles at the early stages of pathogen infection, allowing for early detection of diseases, not limited to fungal diseases, and improving the timeliness and variety of disease monitoring.

[0045] 3) For the first time, the concept of plant health monitoring based on plant volatiles was proposed. The growth status of plants can be judged according to the volatiles in different growth states of plants. Early intervention can be made before pests and diseases infect by improving environmental conditions, enriching plant nutrition, and using immune inducers.

[0046] 4) For the first time, a response processing module was added to plant health and pest and disease monitoring, and a plant immune induction treatment program was added to plant pest and disease treatment, providing the possibility of early prevention of diseases and reducing pesticide use to control pests.

[0047] 5) The establishment of a plant volatile database that is graded, divided by infection stage, and can be automatically captured and updated, and the construction of a plant health and plant pest and disease early warning model based on plant volatiles are innovations in the field of plant volatile utilization, as well as in the fields of plant health, pest and disease monitoring and prevention. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0049] Figure 1 This is a block diagram of the volatile-based plant health monitoring, early warning and processing system provided by the present invention.

[0050] Figure 2 This is a flow chart of the volatile-based plant health monitoring, early warning and treatment method provided by the present invention. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0052] The embodiment of the present invention discloses a plant health monitoring, early warning and processing system based on volatile substances, referring to Figure 1 As shown, it consists of four parts: volatile collection and identification module, volatile integrated analysis module, plant health and plant disease and pest early warning module, and response processing module. The work of each module has clear work guidelines.

[0053] 1. A volatiles collection and identification module, used to collect plant volatiles, environmental parameters, and location information through multi-mode acquisition. In specific implementations, this hardware device comprises a modular collection unit, including a volatiles collection unit, an environmental factor collection unit, and a location information collection unit. Furthermore, the module also includes a data preprocessing unit, which incorporates a bandpass filter circuit (cutoff frequency 0.1-10Hz) within the volatiles collection and identification module to eliminate environmental electromagnetic interference; and a time synchronization unit, which utilizes a GPS clock module (synchronization accuracy ±1ms) to achieve timestamp alignment of multi-sensor data.

[0054] Each volatile matter collection and identification module consists of 1-n volatile matter collection units, 1-n environmental factor collection units, and 1-n location information collection units. According to actual work needs, only a certain number of volatile matter collection units can be retained or different collection units can be combined.

[0055] Furthermore, a volatile database module may be included to store volatile data collected from target crops and recommend specific volatile collection units that match the target crop types and volatile types (in order to select corresponding analysis devices or sensors); this can improve work efficiency and reduce the cost of manually selecting equipment.

[0056] The volatile collection and identification module operates in three primary modes: a fixed field collection device, a handheld collection device, and an equipment-mounted collection device. The volatile collection unit is hardware that can sensitively detect and identify volatile concentrations at or below the ppm level in real time. It consists of a highly sensitive headspace adsorption device combined with a portable gas phase analysis device or sensor. Sensor materials include, but are not limited to, metal oxides, metal nanomaterials, graphene, carbon nanomaterials, and polymers.

[0057] For example, a metal oxide sensor array (covering aldehyde, ketone, and sulfur-containing compound detection, with sensitivities ≥ 5ppb, 10ppb, and 20ppb, respectively) or a biochip (such as a 6-methyl-5-heptene-2-one recognition chip modified with Fusarium graminearum antibodies) is used to convert the gas sample into an n-dimensional feature vector (such as [hexanal concentration, 3-methyl-1-butanol concentration, methyl mercaptan concentration]).

[0058] For example, a sealed air chamber (500mL volume, with a 50-80℃ temperature control module) is deployed in the crop crown, and active sampling is performed through a fan (speed 2000rpm), collecting headspace gas every 15 minutes to continuously monitor VOCs changes in facility agriculture greenhouses or orchards.

[0059] The working guidelines of the volatile collection and identification module include: the need to collect volatiles from at least two stages, namely volatiles in the normal and healthy state of plants and volatiles after plants are infected with pests and diseases. Furthermore, it is necessary to collect volatiles multiple times and at multiple points during the growth process of the plants to form a patrol mode; fixed field collection devices need to be set up at multiple points in the field, handheld collection devices can collect volatiles from different parts of the crops, equipment-carried collection devices can realize large-scale vehicle-mounted volatile collection and can realize large-area, short-time and efficient collection by cruising on drones.

[0060] 2. The volatiles integrated analysis module includes a plant volatiles database and a feedback unit, which is used to analyze volatile data and correlate it with plant health and pest and disease status;

[0061] In specific implementation, this module is mainly software-based, and mainly consists of a plant volatile database and a feedback unit. The plant volatile database includes:

[0062] The health status sub-library records the volatile feature vectors of plant health and sub-health status;

[0063] The pest and disease sub-library stores specific volatile fingerprints according to pathogen types and infection stages;

[0064] The dynamic update unit automatically captures newly disclosed volatile compound data and refines it to health level, number of infection days and environmental conditions. When there is no relevant information for the automatically captured newly disclosed volatile compounds (i.e., it cannot be refined to health level, number of infection days and environmental conditions), a corresponding reminder is issued; and finally a multi-dimensional feature vector is generated.

[0065] Specifically, the plant volatiles database is divided into a health status sub-database and a pest and disease sub-database, covering more than 1,700 existing publicly available volatiles and their relationship with plant pests and diseases. The database also has the function of automatically capturing newly disclosed plant volatiles to fill and enrich the database; and this database will carry out special volatile research and database improvement work specifically for major grains, vegetables, fruit trees, forest plants and their growth status and major diseases. The database refines the volatile data of the growth status of major plants into healthy and sub-healthy levels, and will also distinguish between initial infection, infection 1-n days and other information based on the volatile conditions of the interaction between different pathogens and plants.

[0066] The feedback unit is mainly used to provide real-time feedback on volatile species, plant growth conditions, disease types, disease levels, infection time, and environmental conditions, providing a reference for users to make further decisions. Similarly, a volatile database related to pests and plants is established synchronously based on the situation of pests infecting plants. The feedback unit can be a software that provides feedback results, which can be viewed in real time through a mobile phone or computer. At this time, the positioning and collection system can feedback information on the corresponding position during large-scale or multi-point monitoring; for a certain growth state of a plant or a specific target disease, it can also be a simple but convenient presentation form such as an instant color card. In addition, the feedback unit can use a principal component analysis dimensionality reduction algorithm to reduce the 100+ dimensional volatile parameters to 10-15 dimensions, generate a visual report, and output it through a mobile terminal.

[0067] For example, the volatiles integrated analysis module identifies both plant volatiles and compounds. After collecting the odor of wheat scab, it extracts over 50 parameters, including peak intensity, peak area, and retention time, to construct a "disease odor fingerprint vector" (e.g., [0.8, 1.2, 0.5] for wheat scab, corresponding to the normalized concentrations of three key components). This substance is then matched against pheromones stored in the original database, confirming that it is the compound responsible for wheat scab, signaling the occurrence of wheat scab.

[0068] 3. Plant health and plant pest and disease early warning module, including early warning models and feedback units, used to predict plant health and pest and disease development trends based on analysis results;

[0069] In its implementation, the module is primarily software-based, consisting of 1-n early warning models and a feedback unit. This module combines information such as the concentrations of different volatiles, the number of days of infection by species, and environmental conditions accumulated in the plant volatile database to provide 1-n models for plant growth and the occurrence of pests and diseases over a period of time. The module then incorporates feedback from the feedback unit of the volatile integrated analysis module, including the health status or pest and disease conditions of the target plants monitored, as well as environmental monitoring information about the target plants in the volatile integrated analysis module, into the model to predict and warn of plant growth or pest and disease conditions. The feedback unit presents the results via a mobile phone, computer, or color display card; similarly, the location information unit can be used to feedback the location. Specifically, it can be used to generate multi-level response signals based on the warning level, including: when the concentration of a characteristic component exceeds the healthy mean by N times (e.g., 1.5) times, an initial warning is triggered; when it exceeds 2N times, an emergency warning is triggered; and key contributing components are analyzed using SHAP values, presented via a mobile phone, computer, or color display card, and the warning location is included. The N times can be set according to expert opinions.

[0070] For example, the early warning model includes:

[0071] Long short-term memory network model (LSTM), used for time series prediction of infection stages;

[0072] Support vector machine model (SVM) for disease type classification;

[0073] The input parameters for these two models include volatile concentration gradients, environmental factors, and geographic location data. For example, in the case of wheat fusarium head blight, the cloud-based model identified a three-fold higher-than-average concentration of 3-methyl-1-butanol in wheat ears in a certain area, along with the presence of 6-methyl-5-hepten-2-one, triggering an "urgent warning for fusarium head blight."

[0074] 4. The response processing module includes a control database and a feedback unit to provide targeted cultivation or control suggestions.

[0075] In practice, this module is primarily software-based, consisting of a database for plant health cultivation and pest and disease control, as well as a feedback unit. This database includes: an immune response library, which identifies immune responses tailored to plant health, as well as biological or chemical pest and disease control strategies; and a precision pesticide application library, which matches biopesticides and application coordinates by pest and disease type.

[0076] Specifically, the database contains information on the timing, type, and method of pesticide application based on plant health and pest and disease severity. It provides methods for inducing immunity to sub-healthy plants or those in the early stages of disease infestation, and recommends biopesticides and fungicides for pests and diseases. This database can also pinpoint specific locations. This response processing module can also be used in conjunction with drone-based pesticide application systems, enabling targeted application within ±5 meters using geo-fencing technology.

[0077] The present invention adds a reaction processing module to the plant health and pest and disease monitoring work for the first time, and adds a plant immune induction treatment scheme to the plant pest and disease treatment, providing the possibility for early prevention of diseases and reduction of pesticides and pest control.

[0078] The following four embodiments illustrate the specific application of the plant health monitoring, early warning and treatment system based on volatiles of the present invention:

[0079] Example 1: Application of fixed-point monitoring and immune induction of grain and oil crops in the field;

[0080] In the field grain and oil crop area, fixed-point volatile collection devices were installed, one for every 100 mu (approximately 1.5 acres) of land, for a total of 20 devices across the 2,000 mu (approximately 1.5 acres) area. Each device contains two volatile collection units, each designed to collect two different types of crop volatiles. Due to the construction of high-standard farmland, a meteorological monitoring station has been established in the area, so the collection devices only consist of a volatile collection unit and a location collection unit. Monitoring begins at the initial stage of crop planting, with volatile collection data provided daily. During the early stages of crop vegetative growth, three of the 20 collection devices, after processing by the volatile integrated analysis module, display on mobile devices that the monitored area is experiencing initial bacterial disease infestation. Furthermore, weather forecasts indicate the area is likely to be affected by high temperatures. The response processing module then determines whether to promptly administer immune inducers throughout the monitored area to improve crop disease and heat tolerance. Continuous monitoring by the fixed-point volatile collection devices revealed that the disease infestation was resolved five days after the immune inducer application.

[0081] Example 2: Handheld multi-site monitoring device for greenhouse facilities;

[0082] For seedlings that are about to be transplanted in the greenhouse, a handheld device containing elements for collecting volatile substances in the early stages of plant viral diseases can be used to monitor whether some of the seedlings to be transplanted are already carrying the virus, thereby avoiding losses after transplanting.

[0083] Furthermore, during the growth of greenhouse vegetables, by collecting plant volatiles from the upper, middle and afternoon parts of the plants, it was identified that the roots of some plants were in the early stages of fungal infection. Therefore, fungicide dripping was used to treat the roots, preventing the large-scale occurrence of the disease in advance.

[0084] Example 3: Orchard vehicle-mounted monitoring and drug use guidance;

[0085] During an inspection of the orchard, the vehicle-mounted volatiles collection device discovered that some areas were in the early stages of disease and aphid infestation. However, the early warning model determined that the aphid population would decrease significantly due to recent high temperatures, necessitating further treatment. The feedback processing module recommended spraying with an environmentally friendly fungicide. The orchard-mounted monitoring device was then further transformed into a pesticide application vehicle. Using location information from the location information collection component, it applied precise pesticides to the affected areas.

[0086] Example 4: UAV patrol and precise pesticide delivery in large forest areas;

[0087] In the monitoring of large-scale health conditions and pest and disease conditions in timber forests, an integrated collection device consisting of multiple collection components equipped on drones was used. During the monitoring and inspection of 100 hectares of mountain forests, multiple areas of point-like disease occurrence and multiple areas of sub-healthy forests were found. The overall pest and disease situation in the area was viewed in real time through a computer. Furthermore, drones were used to accurately apply fungicides to treat diseases or plant immune inducers to enhance tree vigor at the corresponding points.

[0088] Based on the same inventive concept, the embodiment of the present invention also provides a plant health monitoring, early warning and treatment method based on volatiles. Using the plant health monitoring, early warning and treatment system based on volatiles as in the above embodiment, the specific steps refer to Figure 2 Shown, including:

[0089] S1. Obtain volatiles, environment and location data through the volatiles collection and identification module;

[0090] S2. Generate plant health or pest and disease status reports by matching the database with the volatiles integrated analysis module;

[0091] S3. Predict plant health and pest and disease development trends through the plant health and pest and disease early warning module;

[0092] S4. Generate prevention and control recommendations and guide their implementation through the response processing module.

[0093] This method collects plant volatile dynamic data in real time and combines it with machine learning models to achieve

[0094] The disease identification time is significantly earlier than traditional methods, and it drives the targeted spraying system to reduce the application of ineffective pesticides. At the same time, the immune induction program is used to improve the stress resistance of crops, and the comprehensive prevention and control efficiency is greatly improved.

[0095] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0096] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A plant health monitoring, early warning and treatment system based on volatiles, characterized in that: It includes the following modules connected in sequence: Volatile collection and identification module, used to collect plant volatiles, environmental parameters and location information through multi-mode; The volatiles integrated analysis module includes a plant volatiles database and a feedback unit for analyzing volatiles data and correlating them with plant health and pest and disease status; Plant health and pest warning module, which includes warning models and feedback units, and is used to predict plant health and pest development trends based on analysis results; The response processing module includes a control database and a feedback unit, which is used to provide targeted cultivation or control suggestions.

2. The system according to claim 1, wherein: The volatile matter collection and identification module includes: At least one volatile matter collection unit, using a headspace adsorption device combined with a portable gas phase analysis device or sensor, wherein the sensor material is selected from metal oxides, metal nanomaterials, graphene, carbon nanomaterials or polymers; At least one environmental factor collection unit for collecting temperature and humidity data; At least one location information collection unit, used to locate the collection point; The volatile database module is used to store the volatile data of the target crops and recommend specific volatile collection units based on the target crop types and volatile types; The working modes of the volatile matter collection and identification module include field fixed, handheld or equipment-carried collection.

3. The system according to claim 2, characterized in that The equipment-carrying collection device is vehicle-mounted or drone-mounted, and is used for large-scale cruise collection.

4. The system according to claim 1, wherein: The plant volatile database includes: The health status sub-library records the volatile feature vectors of plant health and sub-health status; The pest and disease sub-library stores specific volatile fingerprints according to pathogen types and infection stages; The dynamic update unit automatically captures newly disclosed volatile compound data and refines it to health level, infection days and environmental conditions, or issues corresponding reminders when there is no relevant information for the automatically captured newly disclosed volatile compounds; and finally generates a multi-dimensional feature vector.

5. The system according to claim 1, wherein: The feedback unit of the volatiles integrated analysis module uses a principal component analysis dimensionality reduction algorithm to reduce the 100+ dimensional volatiles parameters to 10-15 dimensions, generate a visual report and output it through a mobile terminal.

6. The system according to claim 1, wherein: The early warning model of the plant health and pest and disease early warning module includes: Long short-term memory network model for time series prediction of infection stages; Support vector machine model for disease type classification; The model input parameters include volatile matter concentration gradient, environmental factors and geographical location data.

7. The system according to claim 1, wherein: The feedback unit of the plant health and pest warning module is used to generate a multi-level response signal according to the warning level, including: when the concentration of the characteristic component exceeds the healthy average by N times, an early warning is triggered; when it exceeds 2N times, an emergency warning is triggered; and the key contributing components are analyzed through the SHAP value, and presented in the form of a mobile phone, computer or color card, and the warning location is included.

8. The system according to claim 1, wherein: The control database of the reaction processing module includes: Immunity induction program library, including immunity induction programs targeting plant health status, as well as biological or chemical control programs for pests and diseases; A precise pesticide application plan library that matches biological pesticides and application coordinates according to the type of pests and diseases.

9. The system according to claim 1, wherein: The reaction processing module is also used to work in conjunction with the drone spraying system to achieve targeted spraying through geo-fencing technology.

10. A plant health monitoring, early warning and treatment method based on volatiles, characterized in that: Using the volatile-based plant health monitoring, early warning, and treatment system according to any one of claims 1 to 9 comprises the following steps: S1. Obtain volatiles, environment and location data through the volatiles collection and identification module; S2. Generate plant health or pest and disease status reports by matching the database with the volatiles integrated analysis module; S3. Predict plant health and pest and disease development trends through the plant health and pest and disease early warning module; S4. Generate prevention and control recommendations and guide their implementation through the response processing module.

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